Overview

Dataset statistics

Number of variables13
Number of observations5234
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory572.5 KiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric12

Alerts

density is highly skewed (γ1 = 44.15850322)Skewed

Reproduction

Analysis started2023-12-12 05:11:02.361189
Analysis finished2023-12-12 05:11:28.606864
Duration26.25 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

category
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.8 KiB
white
3962 
red
1272 

Length

Max length5
Median length5
Mean length4.5139473
Min length3

Characters and Unicode

Total characters23626
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowred
2nd rowred
3rd rowred
4th rowred
5th rowred

Common Values

ValueCountFrequency (%)
white 3962
75.7%
red 1272
 
24.3%

Length

2023-12-12T10:41:28.758709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:41:28.953765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
white 3962
75.7%
red 1272
 
24.3%

Most occurring characters

ValueCountFrequency (%)
e 5234
22.2%
w 3962
16.8%
h 3962
16.8%
i 3962
16.8%
t 3962
16.8%
r 1272
 
5.4%
d 1272
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23626
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5234
22.2%
w 3962
16.8%
h 3962
16.8%
i 3962
16.8%
t 3962
16.8%
r 1272
 
5.4%
d 1272
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 23626
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5234
22.2%
w 3962
16.8%
h 3962
16.8%
i 3962
16.8%
t 3962
16.8%
r 1272
 
5.4%
d 1272
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5234
22.2%
w 3962
16.8%
h 3962
16.8%
i 3962
16.8%
t 3962
16.8%
r 1272
 
5.4%
d 1272
 
5.4%

fixed acidity
Real number (ℝ)

Distinct106
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1425497 × 10-15
Minimum-2.5879513
Maximum6.5513151
Zeros0
Zeros (%)0.0%
Negative3202
Negative (%)61.2%
Memory size81.8 KiB
2023-12-12T10:41:29.192757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2.5879513
5-th percentile-1.1528599
Q1-0.62414196
median-0.1709552
Q30.35776269
95-th percentile2.0194475
Maximum6.5513151
Range9.1392664
Interquartile range (IQR)0.98190466

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)8.7531905 × 1014
Kurtosis4.5429741
Mean1.1425497 × 10-15
Median Absolute Deviation (MAD)0.45318676
Skewness1.6530168
Sum6.117773 × 10-12
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:29.504928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3220174516 271
 
5.2%
-0.4730797064 265
 
5.1%
-0.6241419611 244
 
4.7%
-0.2464863242 222
 
4.2%
-0.1709551969 219
 
4.2%
-0.397548579 214
 
4.1%
-0.01989294212 195
 
3.7%
-0.5486108337 191
 
3.6%
-0.0954240695 190
 
3.6%
-0.7752042158 179
 
3.4%
Other values (96) 3044
58.2%
ValueCountFrequency (%)
-2.587951273 1
 
< 0.1%
-2.512420145 1
 
< 0.1%
-2.285826763 2
 
< 0.1%
-2.134764509 3
 
0.1%
-2.059233381 1
 
< 0.1%
-1.983702254 1
 
< 0.1%
-1.908171126 6
 
0.1%
-1.832639999 7
 
0.1%
-1.757108872 5
 
0.1%
-1.681577744 25
0.5%
ValueCountFrequency (%)
6.551315139 1
< 0.1%
6.324721757 2
< 0.1%
6.24919063 1
< 0.1%
5.871534993 1
< 0.1%
5.342817101 1
< 0.1%
5.267285974 1
< 0.1%
5.116223719 1
< 0.1%
4.965161464 1
< 0.1%
4.889630337 1
< 0.1%
4.738568082 1
< 0.1%

volatile acidity
Real number (ℝ)

Distinct184
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.018354 × 10-16
Minimum-1.7185597
Maximum20.599091
Zeros0
Zeros (%)0.0%
Negative3153
Negative (%)60.2%
Memory size81.8 KiB
2023-12-12T10:41:29.789591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.7185597
5-th percentile-1.1269364
Q1-0.6418234
median-0.24750107
Q30.42998633
95-th percentile1.8403103
Maximum20.599091
Range22.317651
Interquartile range (IQR)1.0718097

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-2.4888189 × 1015
Kurtosis55.521327
Mean-4.018354 × 10-16
Median Absolute Deviation (MAD)0.49814745
Skewness3.6942492
Sum-2.1441737 × 10-12
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:30.055673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.311930998 232
 
4.4%
-0.5747849415 221
 
4.2%
-0.4423148547 219
 
4.2%
-0.3768662626 188
 
3.6%
-0.5082849551 187
 
3.6%
-0.7094091075 183
 
3.5%
-0.6418233951 179
 
3.4%
-0.8462585669 178
 
3.4%
-0.183568678 169
 
3.2%
-0.05716616488 166
 
3.2%
Other values (174) 3312
63.3%
ValueCountFrequency (%)
-1.718559748 2
 
< 0.1%
-1.680318554 1
 
< 0.1%
-1.642253182 1
 
< 0.1%
-1.566643489 6
 
0.1%
-1.529096015 4
 
0.1%
-1.491718057 9
 
0.2%
-1.454508089 3
 
0.1%
-1.41746461 31
0.6%
-1.380586136 3
 
0.1%
-1.343871203 37
0.7%
ValueCountFrequency (%)
20.59909117 1
 
< 0.1%
18.21731267 1
 
< 0.1%
4.321242155 1
 
< 0.1%
4.096453584 1
 
< 0.1%
3.90435207 1
 
< 0.1%
3.845841553 1
 
< 0.1%
3.786914586 1
 
< 0.1%
3.747395639 1
 
< 0.1%
3.667787297 1
 
< 0.1%
3.546920861 3
0.1%

citric acid
Real number (ℝ)

Distinct92
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.3711276 × 10-16
Minimum-1.9744575
Maximum30.008279
Zeros0
Zeros (%)0.0%
Negative2877
Negative (%)55.0%
Memory size81.8 KiB
2023-12-12T10:41:30.304449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.9744575
5-th percentile-1.4159556
Q1-0.41243566
median-0.068929496
Q30.41789975
95-th percentile1.2107579
Maximum30.008279
Range31.982737
Interquartile range (IQR)0.83033541

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-7.2939643 × 1015
Kurtosis284.37196
Mean-1.3711276 × 10-16
Median Absolute Deviation (MAD)0.40235619
Skewness10.748921
Sum-7.1764816 × 10-13
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:30.571038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1250737507 265
 
5.1%
-0.0132121977 243
 
4.6%
-0.2386696603 237
 
4.5%
0.8743871864 232
 
4.4%
-0.3540545553 209
 
4.0%
0.0969671628 203
 
3.9%
-0.1816515562 198
 
3.8%
-0.06892949589 188
 
3.6%
-0.4712856867 185
 
3.5%
-0.2961349698 179
 
3.4%
Other values (82) 3095
59.1%
ValueCountFrequency (%)
-1.974457485 31
0.6%
-1.902271783 44
0.8%
-1.830790344 27
0.5%
-1.759999558 36
0.7%
-1.689886205 24
0.5%
-1.620437445 26
0.5%
-1.551640795 30
0.6%
-1.483484123 37
0.7%
-1.415955632 36
0.7%
-1.349043848 42
0.8%
ValueCountFrequency (%)
30.00827922 1
 
< 0.1%
26.78754208 1
 
< 0.1%
18.2668122 1
 
< 0.1%
15.52151221 1
 
< 0.1%
5.120628005 1
 
< 0.1%
3.828735254 1
 
< 0.1%
3.031181954 6
0.1%
2.994456115 1
 
< 0.1%
2.693827713 1
 
< 0.1%
2.577833901 1
 
< 0.1%

residual sugar
Real number (ℝ)

Distinct319
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.3441665 × 10-17
Minimum-0.95531293
Maximum15.311967
Zeros0
Zeros (%)0.0%
Negative3320
Negative (%)63.4%
Memory size81.8 KiB
2023-12-12T10:41:30.817704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.95531293
5-th percentile-0.85022714
Q1-0.70310704
median-0.49293546
Q30.51588809
95-th percentile1.966072
Maximum15.311967
Range16.26728
Interquartile range (IQR)1.2189951

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-2.3021575 × 1016
Kurtosis24.750971
Mean-4.3441665 × 10-17
Median Absolute Deviation (MAD)0.33102023
Skewness2.7660406
Sum-4.2588155 × 10-13
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:31.106775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6610727216 196
 
3.7%
-0.7451413513 195
 
3.7%
-0.7871756661 187
 
3.6%
-0.7031070364 186
 
3.6%
-0.8292099809 169
 
3.2%
-0.7661585087 149
 
2.8%
-0.6190384068 144
 
2.8%
-0.6400555642 143
 
2.7%
-0.682089879 143
 
2.7%
-0.7241241938 143
 
2.7%
Other values (309) 3579
68.4%
ValueCountFrequency (%)
-0.9553129254 1
 
< 0.1%
-0.934295768 7
 
0.1%
-0.9132786106 22
 
0.4%
-0.8922614531 36
 
0.7%
-0.8817528744 3
 
0.1%
-0.8712442957 77
1.5%
-0.860735717 1
 
< 0.1%
-0.8502271383 127
2.4%
-0.8397185596 3
 
0.1%
-0.8292099809 169
3.2%
ValueCountFrequency (%)
15.31196691 1
< 0.1%
14.68145219 1
< 0.1%
12.74787371 1
< 0.1%
6.274589225 1
< 0.1%
5.560005873 1
< 0.1%
4.393553636 1
< 0.1%
3.857616122 1
< 0.1%
3.668461705 1
< 0.1%
3.542358761 1
< 0.1%
3.290152872 2
< 0.1%

chlorides
Real number (ℝ)

Distinct216
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.3892916 × 10-16
Minimum-29.985587
Maximum24.815113
Zeros0
Zeros (%)0.0%
Negative3266
Negative (%)62.4%
Memory size81.8 KiB
2023-12-12T10:41:31.369548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-29.985587
5-th percentile-0.83268799
Q1-0.44678088
median-0.15401689
Q30.33449461
95-th percentile1.1432772
Maximum24.815113
Range54.800701
Interquartile range (IQR)0.78127549

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-4.1857409 × 1015
Kurtosis336.24413
Mean-2.3892916 × 10-16
Median Absolute Deviation (MAD)0.36296416
Skewness3.1624511
Sum-1.1510792 × 10-12
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:31.635255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5179977728 164
 
3.1%
-0.2470912051 157
 
3.0%
-0.1845897497 157
 
3.0%
-0.31151653 155
 
3.0%
-0.3780209789 152
 
2.9%
-0.1540168886 148
 
2.8%
-0.4467808785 142
 
2.7%
-0.123874667 142
 
2.7%
-0.06482353906 140
 
2.7%
-0.5919036643 137
 
2.6%
Other values (206) 3740
71.5%
ValueCountFrequency (%)
-29.98558746 1
 
< 0.1%
-1.967099578 1
 
< 0.1%
-1.71881758 2
 
< 0.1%
-1.645397051 1
 
< 0.1%
-1.575651482 4
0.1%
-1.509152894 3
 
0.1%
-1.445547881 5
0.1%
-1.384540834 5
0.1%
-1.325881689 8
0.2%
-1.269356803 7
0.1%
ValueCountFrequency (%)
24.81511309 1
< 0.1%
24.1501272 1
< 0.1%
21.94225068 1
< 0.1%
5.629769152 1
< 0.1%
5.624275788 1
< 0.1%
4.767365192 1
< 0.1%
4.747619559 2
< 0.1%
4.461779487 1
< 0.1%
4.412317816 2
< 0.1%
4.405206568 2
< 0.1%

free sulfur dioxide
Real number (ℝ)

Distinct137
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-1.6472701
Maximum14.500229
Zeros0
Zeros (%)0.0%
Negative2883
Negative (%)55.1%
Memory size81.8 KiB
2023-12-12T10:41:31.888890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.6472701
5-th percentile-1.3669315
Q1-0.75018676
median-0.133442
Q30.59543818
95-th percentile1.7167923
Maximum14.500229
Range16.147499
Interquartile range (IQR)1.3456249

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)nan
Kurtosis9.6903562
Mean0
Median Absolute Deviation (MAD)0.67281247
Skewness1.3872382
Sum1.3500312 × 10-13
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:32.155340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.07737429094 160
 
3.1%
-1.366931524 139
 
2.7%
-0.2455774082 131
 
2.5%
-0.8623221717 126
 
2.4%
-0.3577128198 126
 
2.4%
0.0347611206 124
 
2.4%
0.2029642379 123
 
2.4%
-0.7501867601 121
 
2.3%
-0.4137805255 120
 
2.3%
-0.1334419967 116
 
2.2%
Other values (127) 3948
75.4%
ValueCountFrequency (%)
-1.647270052 2
 
< 0.1%
-1.591202347 2
 
< 0.1%
-1.535134641 43
 
0.8%
-1.479066935 42
 
0.8%
-1.422999229 106
2.0%
-1.394965376 1
 
< 0.1%
-1.366931524 139
2.7%
-1.310863818 73
1.4%
-1.254796112 69
1.3%
-1.198728406 77
1.5%
ValueCountFrequency (%)
14.50022921 1
< 0.1%
6.510581137 1
< 0.1%
6.062039491 1
< 0.1%
5.641531697 1
< 0.1%
5.47332858 1
< 0.1%
5.249057757 1
< 0.1%
5.164956198 1
< 0.1%
4.940685375 1
< 0.1%
4.856583817 1
< 0.1%
4.632312994 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

Distinct275
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1403437 × 10-16
Minimum-1.9610653
Maximum5.7892121
Zeros0
Zeros (%)0.0%
Negative2535
Negative (%)48.4%
Memory size81.8 KiB
2023-12-12T10:41:32.434110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.9610653
5-th percentile-1.7110563
Q1-0.67530496
median0.021148541
Q30.69974426
95-th percentile1.6104911
Maximum5.7892121
Range7.7502774
Interquartile range (IQR)1.3750492

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-8.7701237 × 1015
Kurtosis-0.2322626
Mean-1.1403437 × 10-16
Median Absolute Deviation (MAD)0.67859572
Skewness0.051673541
Sum-5.4178884 × 10-13
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:32.801191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.08599815157 53
 
1.0%
-0.05028258748 50
 
1.0%
-0.03242480544 48
 
0.9%
-0.3181493181 48
 
0.9%
0.1104374509 48
 
0.9%
0.2175841432 46
 
0.9%
0.02114854069 44
 
0.8%
0.146153015 44
 
0.8%
-0.1038559336 44
 
0.8%
0.164010797 43
 
0.8%
Other values (265) 4766
91.1%
ValueCountFrequency (%)
-1.961065266 2
 
< 0.1%
-1.943207484 4
 
0.1%
-1.925349702 9
 
0.2%
-1.90749192 12
0.2%
-1.889634138 24
0.5%
-1.871776356 17
0.3%
-1.853918574 19
0.4%
-1.836060792 20
0.4%
-1.81820301 23
0.4%
-1.800345228 29
0.6%
ValueCountFrequency (%)
5.789212141 1
< 0.1%
4.476665161 1
< 0.1%
4.074865065 1
< 0.1%
3.521273821 1
< 0.1%
3.42305602 1
< 0.1%
3.342696001 1
< 0.1%
3.181975962 1
< 0.1%
3.092687052 1
< 0.1%
2.967682578 1
< 0.1%
2.89625145 1
< 0.1%

density
Real number (ℝ)

SKEWED 

Distinct991
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5250468 × 10-16
Minimum-0.057726849
Maximum51.853332
Zeros0
Zeros (%)0.0%
Negative5114
Negative (%)97.7%
Memory size81.8 KiB
2023-12-12T10:41:33.166331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.057726849
5-th percentile-0.045501246
Q1-0.034946102
median-0.024157907
Q3-0.014408408
95-th percentile-0.0036806837
Maximum51.853332
Range51.911059
Interquartile range (IQR)0.020537694

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)3.960701 × 1015
Kurtosis2017.8647
Mean2.5250468 × 10-16
Median Absolute Deviation (MAD)0.010195445
Skewness44.158503
Sum1.3358203 × 10-12
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:33.492347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.03584012093 60
 
1.1%
-0.01262475787 58
 
1.1%
-0.03226458485 53
 
1.0%
-0.01708455287 53
 
1.0%
-0.02958387463 52
 
1.0%
-0.009058529357 52
 
1.0%
-0.01440840785 50
 
1.0%
-0.03047735504 50
 
1.0%
-0.01084146509 50
 
1.0%
-0.01886909646 46
 
0.9%
Other values (981) 4710
90.0%
ValueCountFrequency (%)
-0.05772684855 1
< 0.1%
-0.05763722263 1
< 0.1%
-0.05723391718 1
< 0.1%
-0.05642736105 1
< 0.1%
-0.05633774822 2
< 0.1%
-0.05615852525 2
< 0.1%
-0.05562087798 1
< 0.1%
-0.05490406545 1
< 0.1%
-0.05468007336 1
< 0.1%
-0.05400813091 1
< 0.1%
ValueCountFrequency (%)
51.85333197 1
 
< 0.1%
38.8781747 1
 
< 0.1%
32.03345777 1
 
< 0.1%
0.6236509162 9
0.2%
0.1717371198 1
 
< 0.1%
0.04559320598 1
 
< 0.1%
0.01626515068 1
 
< 0.1%
0.01408720842 1
 
< 0.1%
0.01386493946 2
 
< 0.1%
0.01297580813 1
 
< 0.1%

pH
Real number (ℝ)

Distinct106
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.0034277 × 10-16
Minimum-3.3849445
Maximum4.0600108
Zeros0
Zeros (%)0.0%
Negative2674
Negative (%)51.1%
Memory size81.8 KiB
2023-12-12T10:41:33.725586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-3.3849445
5-th percentile-1.5593679
Q1-0.69083855
median-0.041232755
Q30.65574679
95-th percentile1.6987346
Maximum4.0600108
Range7.4449553
Interquartile range (IQR)1.3465853

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-1.9988208 × 1015
Kurtosis0.22437955
Mean-5.0034277 × 10-16
Median Absolute Deviation (MAD)0.64960579
Skewness0.23834299
Sum-2.5663915 × 10-12
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:33.982834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3640836638 158
 
3.0%
0.0228771198 157
 
3.0%
-0.4943118657 149
 
2.8%
-0.429119314 143
 
2.7%
-0.1054950913 142
 
2.7%
0.1506423678 142
 
2.7%
-0.2344800599 138
 
2.6%
-0.1699106155 135
 
2.6%
-0.6251707152 130
 
2.5%
-0.2992041617 127
 
2.4%
Other values (96) 3813
72.9%
ValueCountFrequency (%)
-3.38494453 1
 
< 0.1%
-3.240052496 2
 
< 0.1%
-3.024160805 1
 
< 0.1%
-2.881185341 2
 
< 0.1%
-2.809980295 3
 
0.1%
-2.668130627 1
 
< 0.1%
-2.597484052 3
 
0.1%
-2.527021692 1
 
< 0.1%
-2.45674259 6
0.1%
-2.386645794 8
0.2%
ValueCountFrequency (%)
4.060010764 2
< 0.1%
3.782856544 2
< 0.1%
3.615189204 1
 
< 0.1%
3.559068105 1
 
< 0.1%
3.502830208 2
< 0.1%
3.390002071 1
 
< 0.1%
3.333410846 2
< 0.1%
3.276700858 2
< 0.1%
3.219871605 4
0.1%
3.162922586 3
0.1%

sulphates
Real number (ℝ)

Distinct114
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3455173 × 10-16
Minimum-46.033999
Maximum38.449037
Zeros0
Zeros (%)0.0%
Negative3019
Negative (%)57.7%
Memory size81.8 KiB
2023-12-12T10:41:34.265880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-46.033999
5-th percentile-0.70701361
Q1-0.37642782
median-0.082573778
Q30.24801201
95-th percentile0.98264711
Maximum38.449037
Range84.483036
Interquartile range (IQR)0.62443983

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)2.98936 × 1015
Kurtosis1275.0025
Mean3.3455173 × 10-16
Median Absolute Deviation (MAD)0.29385404
Skewness-7.4374934
Sum1.722622 × 10-12
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:34.496863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1193055326 211
 
4.0%
-0.2662325512 193
 
3.7%
0.02762148612 192
 
3.7%
-0.3396960606 183
 
3.5%
-0.1927690419 171
 
3.3%
-0.5600865886 163
 
3.1%
-0.2295007966 159
 
3.0%
-0.1560372872 158
 
3.0%
-0.04584202322 156
 
3.0%
-0.3029643059 153
 
2.9%
Other values (104) 3495
66.8%
ValueCountFrequency (%)
-46.03399887 1
 
< 0.1%
-1.147794663 1
 
< 0.1%
-1.111062909 1
 
< 0.1%
-1.037599399 4
 
0.1%
-1.000867645 3
 
0.1%
-0.96413589 10
 
0.2%
-0.9274041353 12
 
0.2%
-0.8906723806 12
 
0.2%
-0.853940626 25
0.5%
-0.8172088713 31
0.6%
ValueCountFrequency (%)
38.44903687 1
 
< 0.1%
5.390457668 1
 
< 0.1%
5.316994159 1
 
< 0.1%
5.206798895 2
< 0.1%
3.99465099 1
 
< 0.1%
3.957919236 1
 
< 0.1%
3.884455726 1
 
< 0.1%
3.774260462 1
 
< 0.1%
3.039625369 3
0.1%
2.96616186 1
 
< 0.1%

alcohol
Real number (ℝ)

Distinct111
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.9269873 × 10-16
Minimum-2.1430244
Maximum3.677175
Zeros0
Zeros (%)0.0%
Negative2941
Negative (%)56.2%
Memory size81.8 KiB
2023-12-12T10:41:34.715515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2.1430244
5-th percentile-1.2995173
Q1-0.8777637
median-0.11860725
Q30.72489991
95-th percentile1.8214592
Maximum3.677175
Range5.8201994
Interquartile range (IQR)1.6026636

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-1.687359 × 1015
Kurtosis-0.54740717
Mean-5.9269873 × 10-16
Median Absolute Deviation (MAD)0.75915645
Skewness0.54687039
Sum-3.1599168 × 10-12
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:34.964610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8777636976 290
 
5.5%
-0.962114414 254
 
4.9%
-1.130815847 204
 
3.9%
-0.4560101154 197
 
3.8%
-0.03425653316 195
 
3.7%
0.387497049 174
 
3.3%
-0.6247115482 172
 
3.3%
-1.04646513 164
 
3.1%
-0.1186072496 163
 
3.1%
-0.2873086825 157
 
3.0%
Other values (101) 3264
62.4%
ValueCountFrequency (%)
-2.143024444 2
 
< 0.1%
-1.805621578 4
 
0.1%
-1.721270862 10
 
0.2%
-1.636920146 16
 
0.3%
-1.552569429 48
 
0.9%
-1.468218713 68
1.3%
-1.383867996 58
1.1%
-1.29951728 139
2.7%
-1.257341922 1
 
< 0.1%
-1.215166563 131
2.5%
ValueCountFrequency (%)
3.67717499 1
 
< 0.1%
3.086719975 1
 
< 0.1%
2.9601939 1
 
< 0.1%
2.918018542 9
0.2%
2.833667826 3
 
0.1%
2.749317109 1
 
< 0.1%
2.664966393 5
0.1%
2.580615676 10
0.2%
2.552498771 1
 
< 0.1%
2.538440318 1
 
< 0.1%

quality
Real number (ℝ)

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1746625 × 10-16
Minimum-2.7409404
Maximum22.874674
Zeros0
Zeros (%)0.0%
Negative1935
Negative (%)37.0%
Memory size81.8 KiB
2023-12-12T10:41:35.148860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2.7409404
5-th percentile-0.63610855
Q1-0.63610855
median0.16411137
Q30.16411137
95-th percentile0.85729339
Maximum22.874674
Range25.615614
Interquartile range (IQR)0.80021992

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)1.3939269 × 1015
Kurtosis232.72044
Mean7.1746625 × 10-16
Median Absolute Deviation (MAD)0.69318202
Skewness11.051919
Sum3.7552184 × 10-12
Variance1.0001911
MonotonicityNot monotonic
2023-12-12T10:41:35.348586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.1641113675 2288
43.7%
-0.6361085497 1714
32.7%
0.8572933896 851
 
16.3%
-1.582567827 194
 
3.7%
1.468723266 148
 
2.8%
-2.740940365 27
 
0.5%
2.015665927 5
 
0.1%
22.8746737 1
 
< 0.1%
12.5440958 1
 
< 0.1%
20.83017784 1
 
< 0.1%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
-2.740940365 27
 
0.5%
-1.582567827 194
 
3.7%
-0.6361085497 1714
32.7%
0.1641113675 2288
43.7%
0.8572933896 851
 
16.3%
1.468723266 148
 
2.8%
2.015665927 5
 
0.1%
12.5440958 1
 
< 0.1%
18.72534602 1
 
< 0.1%
20.41235612 1
 
< 0.1%
ValueCountFrequency (%)
22.8746737 1
 
< 0.1%
21.84539513 1
 
< 0.1%
21.58870151 1
 
< 0.1%
20.83017784 1
 
< 0.1%
20.41235612 1
 
< 0.1%
18.72534602 1
 
< 0.1%
12.5440958 1
 
< 0.1%
2.015665927 5
 
0.1%
1.468723266 148
 
2.8%
0.8572933896 851
16.3%

Interactions

2023-12-12T10:41:25.809784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:02.662753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:04.663984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:06.570395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:08.587758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:11.521587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:13.467149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:15.510617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:17.611675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:19.803861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:21.837526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:23.731996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:25.944657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:02.811632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:04.812295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:06.724897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:08.794571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:11.697377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:13.614805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:15.690562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:17.753021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:20.006138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:21.990487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:23.875916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:26.088761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:02.954477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:04.974865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:06.855301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:08.953015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:11.840191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:13.767485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:15.859620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:17.910621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:20.157269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:22.165891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:24.095673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:26.247914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:03.098189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:05.143164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:07.054212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:09.127900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:12.020659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:13.948878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:16.043283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:18.354906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:20.316152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:22.314202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:24.268159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:26.410492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:03.282949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:05.295961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:07.226429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:09.301418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:12.184011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:14.145492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:16.211944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:18.523903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:20.484037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:22.489186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:24.436449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:26.557979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:03.430953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:05.465461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:07.371219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:09.500874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:12.350986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:14.304626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:16.410154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:18.677243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:20.687668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:22.631613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:24.585422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:26.753391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:03.643229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:05.623346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:07.556294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:09.671045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:12.512751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:14.487573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:16.622282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:18.854597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:20.856521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:22.821306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:24.763716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:26.929885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:03.858037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:05.812212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:07.741618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:10.621951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:12.697132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:14.671443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:16.795146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:19.031466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:21.037543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:22.985959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:25.021194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:27.059995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:03.996128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:05.956148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:07.909443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:10.782572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:12.856391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:14.832814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:16.961845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:19.177584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:21.208239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:23.129436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:25.198419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:27.228872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:04.171140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:06.121565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:08.069733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:11.020982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:13.027408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:15.025923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:17.133211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:19.340766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:21.373294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:23.284460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:25.367413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:27.391920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:04.342767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:06.272290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:08.235341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:11.188045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:13.177097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:15.188030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:17.292826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:19.487539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:21.521232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:23.419740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:25.510630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:27.882015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:04.505272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:06.428025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:08.422861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:11.352824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:13.329955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:15.360535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:17.440305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:19.647784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:21.673618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:23.569790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:41:25.665125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-12T10:41:28.124537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:41:28.458232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

categoryfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0red0.4332942.324612-1.760000-0.5980210.919940-0.862322-1.103892-0.0135170.2778060.431671-0.624712-0.636109
1red3.001352-0.3119311.210758-0.6820900.567469-0.750187-0.996745-0.009059-0.3640840.174549-0.6247120.164111
2red0.5088251.535520-1.620437-0.7451410.430371-0.862322-1.014603-0.0161920.530353-0.266233-0.962114-0.636109
3red0.4332941.431378-1.902272-0.6610730.522610-1.198728-1.746772-0.0144080.9048020.137817-0.8777640.857293
4red0.2067001.0011920.2055140.2006310.476923-0.750187-0.246718-0.0099500.8427530.982647-0.034257-0.636109
5red-0.3975491.431378-1.483484-0.7031071.015227-0.862322-0.907456-0.0184230.4043750.027621-1.130816-0.636109
6red0.4332941.587104-0.181652-0.7451411.316597-1.198728-1.550336-0.0117330.2778063.774260-1.215167-0.636109
7red1.2641361.638369-0.834672-0.2827642.2123451.2121830.521166-0.006385-0.3640841.276501-1.130816-0.636109
8red1.2641361.638369-0.772842-0.2617472.1359431.1561150.574740-0.006385-0.2992041.460160-1.130816-0.636109
9red0.962012-0.3119311.210758-0.7031070.9199400.259032-0.228860-0.0139620.5303530.798988-0.0342570.857293
categoryfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
5231white-0.7752040.128891-1.830790-0.8292100.187551-0.0773740.07472238.8781750.0228770.027621-1.215167-0.636109
5232white-0.624142-0.442315-0.4712860.2636820.187551-0.1895100.146153-0.0434430.022877-0.1560371.7371090.857293
5234white-0.9262660.0673351.6659842.260312-0.184590-0.0773740.860464-0.009504-0.494312-0.119306-1.4682190.164111
5236white1.9439160.1899930.7253631.125386-0.446781-0.077374-0.586016-0.019762-2.177440-0.853941-0.371659-1.582568
5237white-0.9262660.0673351.6659842.260312-0.184590-0.0773740.860464-0.009504-0.494312-0.119306-1.46821921.845395
5238white0.131169-0.5082850.2591911.7559000.208947-0.0773741.360482-0.011287-1.423918-0.339696-1.215167-0.636109
5239white-0.095424-1.417465-0.0132120.93623124.150127-0.0773740.824749-0.0170851.151583-0.449891-0.962114-0.636109
5241white0.206700-0.1517870.4179002.8908270.183008-0.0773740.967611-0.000149-1.491558-0.266233-1.299517-0.636109
5242white0.055638-1.343871-0.0132121.945055-29.985587-0.077374-0.121714-0.011733-0.105495-0.670282-1.1308160.164111
5243white-0.095424-1.417465-0.0132120.9362310.048714-0.0773740.824749-0.0170851.151583-0.449891-0.962114-0.636109